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Autophagy can be tumor suppressive as well as promotive in regulation of tumorigenesis and disease progression. Accordingly, the prognostic significance of autophagy key regulator Beclin 1 was varied among different tumors. Here, we detected the clinicopathological and prognostic effect of Beclin 1 in the subtypes of intrahepatic cholangiocarcinoma (ICC) and extrahepatic cholangiocarcinoma (ECC). Beclin 1 expression level was detected by immunohistochemistry staining in 106 ICC and 74 ECC patients. We found that Beclin 1 was lowly expressed in 126 (70%) cholangiocarcinoma patients, consist of 72 ICC and 54 ECC. Moreover, the cholangiocarcinoma patients with lymph node metastasis (N1) had a lower Beclin 1 level than that of N0 subgroup (P=0.012). However, we did not detect any correlations between Beclin 1 and other clinicopathological features, including tumor subtypes, vascular invasion, HBV infection, liver cirrhosis, cholecystolithiasis and TNM stage. Survival analysis showed that, compared with the high expression subset, Beclin 1 low expression was correlated with a poorer 3-year progression-free survival (PFS, 69.1% VS 46.8%, P=041) for cholangiocarcinoma. Importantly, our stratified univariate and multivariate analysis confirmed that Beclin 1 lowly expressed ICC had an inferior PFS as well as overall survival than ECC, particularly than that of Beclin 1 highly expressed ECC patients. Thus, our study demonstrated that Beclin 1low expression, correlated with lymph node metastasis, and might be a negative prognostic biomarker for cholangiocarcinoma. Combined Beclin 1 level with the anatomical location might lead to refined prognosis for the subtypes of ICC and ECC.

Cancer-associated fibroblasts (CAFs) are reported to support tumorigenesis by stimulating angiogenesis, cancer cell proliferation, and invasion in most solid tumors. However, the roles of CAFs in the liver cancer microenvironment have not been thoroughly studied. In our previous study, we successfully isolated CAFs from hepatocellular carcinoma (HCC) (H-CAFs) and proved that H-CAFs suppressed the activation of NK cells and thereby created favorable conditions for HCC progression. In our present study, we found that the proliferation of MHCC97L and Hep3B cells was significantly promoted by treatment with conditioned medium from H-CAFs. Pathological analysis also revealed that H-CAFs increased the proportion of Ki-67 (+) malignant cells and prevented them from undergoing necrosis. Moreover, the concentration of hepatocyte growth factor (HGF) cytokine in the conditioned medium of H-CAFs was higher than conditioned medium from normal skin fibroblasts (NSFs). Anti-HGF significantly reduced the proliferation-promoting capability of H-CAFs. In addition, we found that the abundance of H-CAFs correlated positively with tumor size. These results indicate that H-CAFs are an important factor for promoting the growth of HCC in vitro and in vivo, and that HGF plays a key role in HCC proliferation induced by H-CAFs.

BACKGROUND:Accurate prognostication of locally advanced nasopharyngeal carcinoma (NPC) will benefit patients for tailored therapy. Here, we addressed this issue by developing a mathematical algorithm based on support vector machine (SVM) through integrating the expression levels of multi-biomarkers. METHODOLOGY/PRINCIPAL FINDINGS:Ninety-seven locally advanced NPC patients in a randomized controlled trial (RCT), consisting of 48 cases serving as training set and 49 cases as testing set of SVM models, with 5-year follow-up were studied. We designed SVM models by selecting the variables from 38 tissue molecular biomarkers, which represent 6 tumorigenesis signaling pathways, and 3 EBV-related serological biomarkers. We designed 3 SVM models to refine prognosis of NPC with 5-year follow-up. The SVM1 displayed highly predictive sensitivity (sensitivity, specificity were 88.0% and 81.9%, respectively) by integrating the expression of 7 molecular biomarkers. The SVM2 model showed highly predictive specificity (sensitivity, specificity were 84.0% and 94.5%, respectively) by grouping the expression level of 12 molecular biomarkers and 3 EBV-related serological biomarkers. The SVM3 model, constructed by combination SVM1 with SVM2, displayed a high predictive capacity (sensitivity, specificity were 88.0% and 90.3%, respectively). We found that 3 SVM models had strong power in classification of prognosis. Moreover, Cox multivariate regression analysis confirmed these 3 SVM models were all the significant independent prognostic model for overall survival in testing set and overall patients. CONCLUSIONS/SIGNIFICANCE:Our SVM prognostic models designed in the RCT displayed strong power in refining patient prognosis for locally advanced NPC, potentially directing future target therapy against the related signaling pathways.